基本信息
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Bio
I have two programs of research. Each is summarized below.
Spoken Language Understanding
Our team examines the mechanisms involved in spoken language understanding. How does the mind of a listener translate the sounds emanating from a talker’s mouth into the words intended by the talker? Projects explore how listeners’ knowledge of language influences recognition, and how this ability is accomplished when there are competing talkers. How does the brain know which acoustic bits belong to the talker you want to hear? Analyses of the Buckeye Speech Corpus inform these research questions.
Computational Cognitive Modeling
Accurate inference is fundamental to advancing science. We develop methods for improving inference in cognitive modeling. Early work focused on the use of statistical methods that are applied after data have been collected in an experiment (Bayes Factor, MDL). Our current work is in optimal experimental design, which focuses on improving inference at the front end of an experiment, before data have been collected. How can the the informativeness of data be improved while they are being collected? We have applied active learning in many modeling contexts, which can simultaneously improve inference and make experiments efficient. Most recently this work has expanded to using data-driven methods (Bayesian optimization, Gaussian Proceses) to address a wider range of inference problems in and outside of psychology. This work is done in collaboration with my close friend and colleague Jay Myung
Spoken Language Understanding
Our team examines the mechanisms involved in spoken language understanding. How does the mind of a listener translate the sounds emanating from a talker’s mouth into the words intended by the talker? Projects explore how listeners’ knowledge of language influences recognition, and how this ability is accomplished when there are competing talkers. How does the brain know which acoustic bits belong to the talker you want to hear? Analyses of the Buckeye Speech Corpus inform these research questions.
Computational Cognitive Modeling
Accurate inference is fundamental to advancing science. We develop methods for improving inference in cognitive modeling. Early work focused on the use of statistical methods that are applied after data have been collected in an experiment (Bayes Factor, MDL). Our current work is in optimal experimental design, which focuses on improving inference at the front end of an experiment, before data have been collected. How can the the informativeness of data be improved while they are being collected? We have applied active learning in many modeling contexts, which can simultaneously improve inference and make experiments efficient. Most recently this work has expanded to using data-driven methods (Bayesian optimization, Gaussian Proceses) to address a wider range of inference problems in and outside of psychology. This work is done in collaboration with my close friend and colleague Jay Myung
Research Interests
Papers共 186 篇Author StatisticsCo-AuthorSimilar Experts
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Behavior Research Methodspp.1-12, (2024)
Attention, Perception, & Psychophysicsno. 3 (2023): 879-888
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